Statistics-Left Skewed And Right Skewed Distribution And Relation With Mean, Median And Mode

The Interview question is
Tell Some examples of Left skewed distribution and right skewed distribution and what is the relation between mean, median and mode in these distribution.

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#data-science

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How to Find Mean, Median, and Mode in Python?

Mean, median, and mode are fundamental topics of statistics. You can easily calculate them in Python, with and without the use of external libraries.

These three are the main measures of central tendency. The central tendency lets us know the “normal” or “average” values of a dataset. If you’re just starting with data science, this is the right tutorial for you.

By the end of this tutorial you’ll:

• Understand the concept of mean, median, and mode
• Be able to create your own mean, median, and mode functions in Python
• Make use of Python’s statistics module to quickstart the use of these measurements

If you want a downloadable version of the following exercises, feel free to check out the GitHub repository.

Let’s get into the different ways to calculate mean, median, and mode.

#development #python #how to find mean, median, and mode in python #find mean #median #mode

Statistics-Left Skewed And Right Skewed Distribution And Relation With Mean, Median And Mode

The Interview question is
Tell Some examples of Left skewed distribution and right skewed distribution and what is the relation between mean, median and mode in these distribution.

Subscribe: https://www.youtube.com/channel/UCNU_lfiiWBdtULKOw6X0Dig

#data-science

Mean, Median and Mode with JavaScript

In this video, we are implementing algorithms for calculating the mean, median and mode of a dataset with JavaScript.

Before proceeding to coding, we briefly discuss how we can calculate each of these dataset properties.

Mean: in order to calculate the mean, all we have to do is to sum up all data points and divide by the number of data points. In other words, the mean, and more specifically the arithmetic mean (which is what we are calculating today), is what we usually call “the average” in everyday-life. Although, technically, mean, median, and mode are all kinds of averages, each trying to summarize the dataset with a single number representing a “typical” data point from the dataset.

Median: in order to get the median (the middle value), we first need to sort the dataset (conventionally in ascending order, but we would get the same result by sorting in descending order) and pick the data-point in the middle. If we had an even number of data points, we would get the mean of the 2 middle elements (by adding them and dividing by 2).

Mode: is the value in the dataset which occurs more frequently. If all values in the dataset appear with the same frequency, then the dataset has no mode. It is also possible for a dataset to have more than one modes.

Mean, median and mode, are different approaches for calculating the “typical” or “central” value of a dataset. That’s why they are also called measures of central tendency. Usually, they are used in conjunction with measures of spread (or measures of dispersion), in order to get the degree to which data is distributed around the center. Such measures of spread are the Range (which is the difference between the maximum and minimum value of the dataset) and Variance (or Standard Deviation which is the square root of Variance).

In this video, we are implementing algorithms for measures of central tendency. Feel free to expand on this, maybe by also calculating measures of spread and/or other statistical values. You could even take it a step further by creating a user interface such as a statistics calculator, which allows the user to enter a dataset and calculates and presents to the user a number of statistical values for the given dataset.

00:00 - Introduction to Concepts
04:15 - Implementation of Algorithms

Enjoy 😊

Don’t forget to like, share, subscribe and I would love to hear your thoughts in the comments section below!

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Suggested Videos:
JavaScript Loops: https://www.youtube.com/watch?v=rTDAAhUgJZM
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Data Visualization with Chart.js: https://www.youtube.com/watch?v=DSlUGncFxa4
Currency Converter with HTML, CSS and JavaScript: https://www.youtube.com/watch?v=ARBvYDNeNdM&list=PLdGqEpyfYoMCSMLcavLm4BmTiZoBnme-O

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#javascript #median #mode #mean

Authentication In MEAN Stack - A Quick Guide

I consider myself an active StackOverflow user, despite my activity tends to vary depending on my daily workload. I enjoy answering questions with angular tag and I always try to create some working example to prove correctness of my answers.

To create angular demo I usually use either plunker or stackblitz or even jsfiddle. I like all of them but when I run into some errors I want to have a little bit more usable tool to undestand what’s going on.

Many people who ask questions on stackoverflow don’t want to isolate the problem and prepare minimal reproduction so they usually post all code to their questions on SO. They also tend to be not accurate and make a lot of mistakes in template syntax. To not waste a lot of time investigating where the error comes from I tried to create a tool that will help me to quickly find what causes the problem.

Angular demo runner
Online angular editor for building demo.
ng-run.com
<>

Let me show what I mean…

Template parser errors#

There are template parser errors that can be easy catched by stackblitz

It gives me some information but I want the error to be highlighted

#mean stack #angular 6 passport authentication #authentication in mean stack #full stack authentication #mean stack example application #mean stack login and registration angular 8 #mean stack login and registration angular 9 #mean stack tutorial #mean stack tutorial 2019 #passport.js

Wlapi: Ruby Based API for The Project Wortschatz Leipzig

WLAPI

DESCRIPTION

WLAPI is a programmatic API for web services provided by the project Wortschatz, University of Leipzig. These services are a great source of linguistic knowledge for morphological, syntactic and semantic analysis of German both for traditional and Computational Linguistics (CL).

Use this API to gain data on word frequencies, left and right neighbours, collocations and semantic similarity. Check it out if you are interested in Natural Language Processing (NLP) and Human Language Technology (HLT).

This library is a set of Ruby bindings for the following featuren. You may also be interested in other language specific bindings:

Perl

Python

PHP

C#

The original Java based clients with many examples can be found on the project overview page.

Implemented Features

You can use the following search methods:

#baseform;

#cooccurrences;

#cooccurrences_all;

#crossword;

#domain;

#experimental_synonyms;

#frequencies;

#left_collocation_finder;

#left_neighbours;

#right_collocation_finder;

#right_neighbours;

#sentences;

#similarity;

#synonyms;

#thesaurus;

#wordforms;

#ngrams;

#ngram_references;

The services NGrams and NGramReferences are under development and will be available soon. Both methods throw an NotImplementedError for now.

The interface will be slightly changed in the version 1.0 to be more readable. For example, #cooccurrences_all may become #all_cooccurrences.

There are two additional services by Wortschatz Leipzig: MARS and Kookurrenzschnitt. They will not be implemented due to internal restrictions of the service provider.

INSTALLATION

WLAPI is provided as a .gem package. Simply install it via RubyGems.

To install WLAPI ussue the following command:

$ gem install wlapi

The current version of WLAPI works with the second Savon generation. You might want to install versions prior to 0.8.0, if you are bound on the old implementations of savon:

$ gem install wlapi -v 0.7.4

If you want to do a system wide installation, do this as root (possibly using sudo).

Alternatively use your Gemfile for dependency management.

We are working on a .deb package, which will be released soon.

SYNOPSIS

Basic usage is very simple:

$ require 'wlapi'
$ api = WLAPI::API.new
$ api.synonyms('Haus', 15) # returns an array with string values (UTF8 encoded)
$ api.domain('Auto') # => Array

If you are going to send mass requests, please contact the support team of the project Wortschatz, get your private credentials and instantiate an authenticated client:

$ require 'wlapi'
$ api = WLAPI::API.new(username, password)

See documentation in the WLAPI::API class for details on particular search methods.

EXCEPTION HIERARCHY

While using WLAPI you can face following errors:

WLAPI::UserError;

WLAPI::ExternalError.

The errors here are presented in the order they may occur during WLAPI's work.

First WLAPI checks the user input and throws a WLAPI::UserError if the arguments are not appropriate.

Then it fetches a response from a remote server, it can result in a WLAPI::ExternalError. In most cases it will be a simple wrapper around other errors, e.g. Savon::SOAP::Fault.

All of them are subcalsses of WLAPI::Error which is in turn a subclass of the standard RuntimeError.

If you want to intercept any and every exception thrown by WLAPI simply rescue WLAPI::Error.

SUPPORT

If you have question, bug reports or any suggestions, please drop me an email :) Any help is deeply appreciated!

If you need some new functionality please contact me or provide a pull request. You code should be complete and tested. Please use local_* and remote_* naming convention for your tests.

Supported Ruby Versions

The library is testend on the following Ruby interpreters:

MRI 1.8.7

MRI 1.9.3

MRI 2.0.x

MRI 2.1.x

JRuby (both 1.8 and 1.9 modes)

Rubinius

CHANGELOG

For details on future plan and working progress see CHANGELOG.

CAUTION

This library is work in process! Though the interface is mostly complete, you might face some not implemented features.

Please contact me with your suggestions, bug reports and feature requests.

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DISCLAIMER We are working on the new RESTful client. Please be patient!

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Author: Arbox
Source Code: https://github.com/arbox/wlapi 
License: MIT license

#ruby #nlp #naturallanguageprocessing